This paper shows that approximate dynamic programming can produce robust strategies in military airlift operations. Simulations are run using randomness in demands and aircraft availability. When demands are uncertain, we vary the degree to which the demands become known in advance. The results show that if we allocate aircraft using approximate dynamic programming, the effect of uncertainty is significantly reduced. These results call into question simulations that examine the effect of advance information which do not use robust decision-making, a property that we feel reflects natural human behavior.

There is a growing sense that the modeling of complex problems in transportation and logistics require drawing on the skills of both mathematical programming and simulation. This paper accomplishes this, combining the modeling and algorithmic framework of approximate dynamic programming with pattern matching and proximal point algorithms. The paper is organized along the theme of modeling not just the physical process, but also the organization and flow of information and decisions. The paper proposes that a decision function can be a modeling choice, and shows how you can use four classes of information to build a family of decision functions.